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Updated: Apr 29, 2026

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基于改进的CycleGAN模型的交叉模式转移学习,用于在超声图像中准确的脏细分

Shuaizi Guo1, Haijie Chen1, Xiangyu Sheng1

  • 1School of Computer Science and Technology, Nanjing Tech University, Nanjing, China.

Ultrasound in medicine & biology
|August 24, 2024
PubMed
概括

这项研究介绍了Seg-CycleGAN,这是一种新的交叉模式转移学习方法,用于生成合成脏超声波图像,以改善细分. 该方法提高了对有限的临床数据的细分精度,降低了注释成本.

关键词:
CT 数据集CT数据集CT数据集这是一个CycleGAN模型.脏美国图像.细分网络的细分网络是一个细分网络.转移学习转移学习

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